Open Access

ARTICLE

Face-Pedestrian Joint Feature Modeling with Cross-Category Dynamic Matching for Occlusion-Robust Multi-Object Tracking

Qin Hu, Hongshan Kong^*

The School of Cryptography Engineering, Information Engineering University, Zhengzhou, 450001, China

* Corresponding Author: Hongshan Kong. Email:

(This article belongs to the Special Issue: Secure & Intelligent Cloud-Edge Systems for Real-Time Object Detection and Tracking)

Computers, Materials & Continua 2026, 86(1), 1-31. https://doi.org/10.32604/cmc.2025.069078

Received 13 June 2025; Accepted 13 August 2025; Issue published 10 November 2025

Abstract

To address the issues of frequent identity switches (IDs) and degraded identification accuracy in multi object tracking (MOT) under complex occlusion scenarios, this study proposes an occlusion-robust tracking framework based on face-pedestrian joint feature modeling. By constructing a joint tracking model centered on “intra-class independent tracking + cross-category dynamic binding”, designing a multi-modal matching metric with spatio-temporal and appearance constraints, and innovatively introducing a cross-category feature mutual verification mechanism and a dual matching strategy, this work effectively resolves performance degradation in traditional single-category tracking methods caused by short-term occlusion, cross-camera tracking, and crowded environments. Experiments on the Chokepoint_Face_Pedestrian_Track test set demonstrate that in complex scenes, the proposed method improves Face-Pedestrian Matching F1 area under the curve (F1 AUC) by approximately 4 to 43 percentage points compared to several traditional methods. The joint tracking model achieves overall performance metrics of IDF1: 85.1825% and MOTA: 86.5956%, representing improvements of 0.91 and 0.06 percentage points, respectively, over the baseline model. Ablation studies confirm the effectiveness of key modules such as the Intersection over Area (IoA)/Intersection over Union (IoU) joint metric and dynamic threshold adjustment, validating the significant role of the cross-category identity matching mechanism in enhancing tracking stability. Our_model shows a 16.7% frame per second (FPS) drop vs. fairness of detection and re-identification in multiple object tracking (FairMOT), with its cross-category binding module adding aboute 10% overhead, yet maintains near-real-time performance for essential face-pedestrian tracking at small resolutions.

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Keywords

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